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Learning Theory

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Cover of 'Learning Theory'

Table of Contents

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    Book Overview
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    Chapter 1 Random Multivariate Search Trees
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    Chapter 2 On Learning and Logic
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    Chapter 3 Predictions as Statements and Decisions
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    Chapter 4 A Sober Look at Clustering Stability
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    Chapter 5 PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption
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    Chapter 6 Stable Transductive Learning
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    Chapter 7 Uniform Convergence of Adaptive Graph-Based Regularization
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    Chapter 8 The Rademacher Complexity of Linear Transformation Classes
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    Chapter 9 Function Classes That Approximate the Bayes Risk
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    Chapter 10 Functional Classification with Margin Conditions
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    Chapter 11 Significance and Recovery of Block Structures in Binary Matrices with Noise
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    Chapter 12 Maximum Entropy Distribution Estimation with Generalized Regularization
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    Chapter 13 Unifying Divergence Minimization and Statistical Inference Via Convex Duality
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    Chapter 14 Mercer’s Theorem, Feature Maps, and Smoothing
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    Chapter 15 Learning Bounds for Support Vector Machines with Learned Kernels
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    Chapter 16 On Optimal Learning Algorithms for Multiplicity Automata
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    Chapter 17 Exact Learning Composed Classes with a Small Number of Mistakes
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    Chapter 18 DNF Are Teachable in the Average Case
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    Chapter 19 Teaching Randomized Learners
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    Chapter 20 Memory-Limited U-Shaped Learning
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    Chapter 21 On Learning Languages from Positive Data and a Limited Number of Short Counterexamples
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    Chapter 22 Learning Rational Stochastic Languages
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    Chapter 23 Parent Assignment Is Hard for the MDL, AIC, and NML Costs
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    Chapter 24 Uniform-Distribution Learnability of Noisy Linear Threshold Functions with Restricted Focus of Attention
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    Chapter 25 Discriminative Learning Can Succeed Where Generative Learning Fails
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    Chapter 26 Improved Lower Bounds for Learning Intersections of Halfspaces
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    Chapter 27 Efficient Learning Algorithms Yield Circuit Lower Bounds
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    Chapter 28 Optimal Oracle Inequality for Aggregation of Classifiers Under Low Noise Condition
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    Chapter 29 Aggregation and Sparsity Via ℓ 1 Penalized Least Squares
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    Chapter 30 A Randomized Online Learning Algorithm for Better Variance Control
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    Chapter 31 Online Learning with Variable Stage Duration
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    Chapter 32 Online Learning Meets Optimization in the Dual
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    Chapter 33 Online Tracking of Linear Subspaces
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    Chapter 34 Online Multitask Learning
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    Chapter 35 The Shortest Path Problem Under Partial Monitoring
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    Chapter 36 Tracking the Best Hyperplane with a Simple Budget Perceptron
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    Chapter 37 Logarithmic Regret Algorithms for Online Convex Optimization
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    Chapter 38 Online Variance Minimization
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    Chapter 39 Online Learning with Constraints
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    Chapter 40 Continuous Experts and the Binning Algorithm
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    Chapter 41 Competing with Wild Prediction Rules
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    Chapter 42 Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path
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    Chapter 43 Ranking with a P-Norm Push
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    Chapter 44 Subset Ranking Using Regression
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    Chapter 45 Active Sampling for Multiple Output Identification
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    Chapter 46 Improving Random Projections Using Marginal Information
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    Chapter 47 Efficient Algorithms for General Active Learning
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    Chapter 48 Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints
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Citations

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Title
Learning Theory
Published by
Springer Science & Business Media, June 2006
DOI 10.1007/11776420
ISBNs
978-3-54-035294-5, 978-3-54-035296-9
Editors

Simon, Hans Ulrich, Lugosi, Gábor

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 8%
Unknown 12 92%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 8%
Student > Ph. D. Student 1 8%
Researcher 1 8%
Unknown 10 77%
Readers by discipline Count As %
Computer Science 3 23%
Unknown 10 77%